[1]} used svm and a two-step classification then regression approach with 167 and 117 initial training datapoints, respectively, to predict new high Curie temperature (\(T_C\) ) xBi[Mey'Mey”]O3–(1-x)PbTiO3 perovskite compounds through 5 iterations of ad. Of the 10 compounds they experimentally synthesized, 6 were perovskites. With an initial approach using only regression and no classification, a perovskite was predicted and synthesized, but discovered to be non-pure. The classification algorithm includes training data from non-pure perovskites and is aimed at identifying promising regions in the four-parameter design space (\(x\) , \(y\) , Mey', and Mey” in xBi[Mey'Mey”]O3–(1-x)PbTiO3) that are more likely to produce pure perovskite phases. The regression step is then aimed at identifying specific compositions with high \(T_C\)  for ferroelectric applications. In the ad scheme, only compositions which are classified as perovskites are updated in the regression model, and a ego scheme [2]} is used to identify new compositions for synthesis (fig:balachandran2018-feedback). Since only a single iteration was used for the regression-only approach before switching to a two-step approach, it is unclear to what extent the classification algorithm affected the regression model and subsequent success of choosing high \(T_C\)  candidates. However, of the six discovered perovskites, 0.2 Bi(Fe0.12 Co0.88)O3 – 0.8 PT had the highest experimental \(T_C\)  of 898, and three were novel Mey'Mey” pairs: FeCo, CoAl, and NiSn. For comparison, the highest and median \(T_C\)  perovskites in the training data are approximately 1100 and 750, respectively.
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